Information System for Ukrainian Text Voiceover Based on Nlp and Machine Learning Methods

2023;
: pp. 1 - 22
1
Lviv Polytechnic National University, Information Systems and Networks Department
2
Ivan Franko National University of Lviv, Applied Mathematics Department
3
Lviv Polytechnic National University, Information Systems and Networks Department
4
Lviv Polytechnic National University, Ukraine
5
Osnabrück University, Institute of Computer Science; Zhytomyr Ivan Franko State University, Professional and Pedagogical, Apecial Education, Andragogy and Management Department

During the research, an information system for voicing Ukrainian-language text was developed based on NLP and machine learning methods. The created information system is implemented in the form of a desktop application, which allows the process of voicing the Ukrainian-language text. The created system included all stages of software development: the design process, the implementation process, and the testing process. For the feasibility of creating this system, already existing software solutions on the market were analysed, their advantages and disadvantages were listed, which were subsequently taken into account to create a new system. During the system analysis of the system, a goal tree, a decision tree, and examples of context diagrams with process decomposition are given. One of the stages of the design of the economic part, where the budget that will be spent on the implementation of the system is analysed, all tax and administrative costs are calculated, development strategies are also analysed and the development strategy of the existing product with accompanying solutions and the product development strategy are selected. After that, an assessment was made for the feasibility of creating the designed system, it’s payback and profit. The object of the research is the process of the voiceover system of the Ukrainian-language text based on NLP and machine learning methods. The subject of the research is the methods and means of the Ukrainian-language text voicing system process based on NLP and machine learning methods. The purpose of the research is to create an information system for voicing Ukrainian- language text based on NLP and machine learning methods. The result of the work is a ready-to- implement information system for voicing Ukrainian-language text based on NLP and machine learning methods, an analytical review of literary and online sources related to the topic of voicing Ukrainian- language text based on NLP and machine learning methods, a systematic analysis of the research object, analysis and selection of software tools for system implementation, practical implementation of the system, economic justification of system implementation activities.

  1. Shieldt, G. C. The complete reference. NY: Osborne McGraw-Hill, 1989.
  2. Martin, Robert C. Clean code: a handbook of agile software craftsmanship. Pearson Education, 2009.
  3. De Micheli, Giovanni, Rolf Ernst, and Wayne Wolf. Readings in hardware/software co-design. Morgan Kaufmann, 2002.
  4. Karan, B., Mahto, K., & Sahu, S. S. (2019). Intelligent Speech Processing in the Time-Frequency Domain.In Intelligent Speech Signal Processing, 153–173. Academic Press. https://doi.org/10.1016/B978-0-12-818130-0.00009-X
  5. Senior, Andrew W., and Anthony Robinson (1995). “Forward-backward retraining of recurrent neural networks”. Advances in Neural Information Processing Systems, 8.
  6. Richter, Jeffrey. CLR via c#. Vol. 4. Redmond: Microsoft Press, 2006.
  7. Nagel, Christian. Professional C# and. Net. John Wiley & Sons, 2021.
  8. Matthew Mcdonald. Pro WPF 4.5 in C#: Windows Presentation Foundation in .NET 4.5, 2012.
  9. Van Santen, J. P., Sproat, R., Olive, J., & Hirschberg, J. (Eds.). (2013). Progress in speech synthesis. Springer Science & Business Media.
  10. Ning, Y., He, S., Wu, Z., Xing, C., & Zhang, L. J. (2019). A review of deep learning based speech synthesis. Applied Sciences, 9(19), 4050. https://doi.org/10.3390/app9194050
  11. Schroeder, M. R. (1993). A brief history of synthetic speech. Speech communication, 13(1–2), 231–237. https://doi.org/10.1016/0167-6393(93)90074-U
  12. Klatt, D. H. (1987). Review of text-to-speech conversion for English. The Journal of the Acoustical Society of America, 82(3), 737–793. https://doi.org/10.1121/1.395275
  13. Flanagan, James L. (2013). Speech analysis synthesis and perception. Springer Science & Business Media,Vol. 3.
  14. Isewon, I., Oyelade, J., & Oladipupo, O. (2014). Design and implementation of text to speech conversion for visually impaired people. International Journal of Applied Information Systems, 7(2), 25–30. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=ed030474fe850f1c0ab4e8005af00e0671d14ffa
  15. Whitchurch, G. G., & Constantine, L. L. (1993). Systems theory. In Sourcebook of family theories and methods: A contextual approach, 325–355. Boston, MA: Springer US. https://doi.org/10.1007/978-0-387-85764-0_14
  16. Anderson, Brian DO, and Sumeth Vongpanitlerd. Network analysis and synthesis: a modern systems theory approach. Courier Corporation, 2013.
  17. Cronholm, Stefan. Why CASE Tools in Information Systems Development?: An Empirical Study Concerning Motives for Investing in CASE Tools. Linköping University, Department of Computer and Information Science, 1995.
  18. Whitman, L., Huff, B., & Presley, A. (1997, December). Structured models and dynamic systems analysis: the integration of the IDEF0/IDEF3 modeling methods and discrete event simulation. In Proceedings of the 29th conference on Winter simulation, 518–524. https://dl.acm.org/doi/pdf/10.1145/268437.268559
  19. Bublyk, M., Kalynii, T., Varava, L., Vysotska, V., Chyrun, L., & Matseliukh, Y. (2022). Decision Support System Design For Low-Voice Emergency Medical Calls At Smart City Based On Chatbot Management In Social Networks. Webology (ISSN: 1735-188X), 19(2).
  20. Dokhnyak, B., & Vysotska, V. (2021). Intelligent Smart Home System Using Amazon Alexa Tools. In MoMLeT+ DS, CEUR workshop proceedings, 441–464. https://ceur-ws.org/Vol-2917/paper33.pdf
  21. Vysotska, V., Holoshchuk, S., & Holoshchuk, R. (2021). A Comparative Analysis for English and Ukrainian Texts Processing Based on Semantics and Syntax Approach. In COLINS, CEUR workshop proceedings, 311–356.    https://ceur-ws.org/Vol-2870/paper26.pdf
  22. Aksonov, D., Gozhyj, A., Kalinina, I., & Vysotska, V. (2021, September). Question-Answering Systems Development Based on Big Data Analysis. In 2021 IEEE 16th International Conference on Computer Sciences and Information Technologies (CSIT), Vol. 1,  113–118. IEEE. DOI: 10.1109/CSIT52700.2021.9648631
  23. Lytvyn, V., Sharonova, N., Hamon, T., Vysotska, V., Grabar, N., & Kowalska-Styczen, A. (2018). Computational linguistics and intelligent systems. In CEUR workshop proceedings, Vol. 2136. https://ceur-ws.org/Vol- 3171/preface.pdf
  24. Lytvyn, V., Vysotska, V., Mykhailyshyn, V., Peleshchak, I., Peleshchak, R., & Kohut, I. (2019, July). Intelligent system of a smart house. In 2019 3rd International Conference on Advanced Information and Communications Technologies (AICT), 282–287. IEEE. DOI: 10.1109/AIACT.2019.8847748
  25. Voloshyn, S., Vysotska, V., Markiv, O., Dyyak, I., Budz, I., & Schuchmann, V. (2022, November). Sentiment Analysis Technology of English Newspapers Quotes Based on Neural Network as Public Opinion Influences Identification Tool. In 2022 IEEE 17th International Conference on Computer Sciences and Information Technologies (CSIT), 83–88. IEEE. DOI: 10.1109/CSIT56902.2022.10000627
  26. Kravets, P., Burov, Y., Oborska, O., Vysotska, V., Dzyubyk, L., & Lytvyn, V. (2021). Stochastic Game Model of Data Clustering. In IntelITSIS, CEUR workshop proceedings, 198–213. https://ceur-ws.org/Vol- 2853/paper19.pdf
  27. Shakhovska, N., Vysotska, V., & Chyrun, L. (2017). Intelligent systems design of distance learning realization for modern youth promotion and involvement in independent scientific researches. In Advances in Intelligent Systems and Computing: Selected Papers from the International Conference on Computer Science and Information Technologies, CSIT 2016, September 6–10 Lviv, Ukraine, 175–198. Springer International Publishing. https://doi.org/10.1007/978-3-319-45991-2_12
  28. Lytvyn, V., et. al. (2019, September). A smart home system development. In Conference on Computer Science and Information Technologies, 804–830. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-33695-0_54
  29. Vysotska, V., Markiv, O., Teslia, S., Romanova, Y., & Pihulechko, I. (2022). Correlation Analysis of Text Author Identification Results Based on N-Grams Frequency Distribution in Ukrainian Scientific and Technical Articles. In CEUR Workshop Proceedings, Vol. 3171, 277–314. CEUR-WS. https://ceur-ws.org/Vol-3171/paper25.pdf
  30. Tymoshenko, K., Vysotska, V., Kovtun, O. V., Holoshchuk, R., & Holoshchuk, S. (2021). Real-Time Ukrainian Text Recognition and Voicing. In COLINS, CEUR workshop proceedings, 357–387. https://ceur-ws.org/Vol- 2870/paper27.pdf